In LapSRN [1]}, LR images are progressively reconstructed in the pyramid networks to obtain the large-scale results, where the intermediate results can be taken directly as the corresponding multiple factors results. In [2]}, Lim et al. found the inter-related phenomenon among multiple scales tasks, i.e., initializing the high-scale model parameters with the pre-trained low-scale network can accelerate the training process and improve the performance. Therefore, they proposed the scale-specific processing modules at the head and tail of the model to handle different upsampling factors. To further exploit the inter-scale correlation between different upsampling factors, Li et al. further optimized the strategy in MDCN [3]}. Different from MDSR which introduces the scale-specific processing strategy both at the head and tail of the model, MDCN can maximize the reuse of model parameters and learn the inter-scale correlation.
